{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YMJZEK5GQP5TWS4ZDLKGNC5OKH","short_pith_number":"pith:YMJZEK5G","schema_version":"1.0","canonical_sha256":"c313922ba683fb3b4b991ad4668bae51d536aac07fc4110621455ea85839b2e0","source":{"kind":"arxiv","id":"2406.05036","version":3},"attestation_state":"computed","paper":{"title":"TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Fobao Zhou, Hang Zhao, Jiayu Yang, Ninghui Feng, Songning Lai, Zhenxiao Yin","submitted_at":"2024-06-07T15:58:12Z","abstract_excerpt":"Time series forecasting has become an increasingly popular research area due to its critical applications in various real-world domains such as traffic management, weather prediction, and financial analysis. Despite significant advancements, existing models face notable challenges, including the necessity of manual hyperparameter tuning for different datasets, and difficulty in effectively distinguishing signal from redundant features in data characterized by strong seasonality. These issues hinder the generalization and practical application of time series forecasting models. To solve this is"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2406.05036","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-07T15:58:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1fbc6657c807bb6e38ef782c1470f93e81b19119a6addcea72ec9f407e75be43","abstract_canon_sha256":"5f5840a46ef1924909da9beb96c59ce6d351043fe3a6df70e77b49fdfa0c1f80"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:57:38.857090Z","signature_b64":"HxfDmf4zLxxlDNYqeGuUD83W/2zAKyIYuI0rl1leGwsGBs+M+fnKM7znEqhFHh95MwEZ5aFXjcQ3LDFxQoHdCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c313922ba683fb3b4b991ad4668bae51d536aac07fc4110621455ea85839b2e0","last_reissued_at":"2026-07-05T08:57:38.856597Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:57:38.856597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Fobao Zhou, Hang Zhao, Jiayu Yang, Ninghui Feng, Songning Lai, Zhenxiao Yin","submitted_at":"2024-06-07T15:58:12Z","abstract_excerpt":"Time series forecasting has become an increasingly popular research area due to its critical applications in various real-world domains such as traffic management, weather prediction, and financial analysis. Despite significant advancements, existing models face notable challenges, including the necessity of manual hyperparameter tuning for different datasets, and difficulty in effectively distinguishing signal from redundant features in data characterized by strong seasonality. These issues hinder the generalization and practical application of time series forecasting models. To solve this is"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05036","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2406.05036/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2406.05036","created_at":"2026-07-05T08:57:38.856658+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.05036v3","created_at":"2026-07-05T08:57:38.856658+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05036","created_at":"2026-07-05T08:57:38.856658+00:00"},{"alias_kind":"pith_short_12","alias_value":"YMJZEK5GQP5T","created_at":"2026-07-05T08:57:38.856658+00:00"},{"alias_kind":"pith_short_16","alias_value":"YMJZEK5GQP5TWS4Z","created_at":"2026-07-05T08:57:38.856658+00:00"},{"alias_kind":"pith_short_8","alias_value":"YMJZEK5G","created_at":"2026-07-05T08:57:38.856658+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.06907","citing_title":"Deep Learning and Foundation Models for Weather Prediction: A Survey","ref_index":2022,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YMJZEK5GQP5TWS4ZDLKGNC5OKH","json":"https://pith.science/pith/YMJZEK5GQP5TWS4ZDLKGNC5OKH.json","graph_json":"https://pith.science/api/pith-number/YMJZEK5GQP5TWS4ZDLKGNC5OKH/graph.json","events_json":"https://pith.science/api/pith-number/YMJZEK5GQP5TWS4ZDLKGNC5OKH/events.json","paper":"https://pith.science/paper/YMJZEK5G"},"agent_actions":{"view_html":"https://pith.science/pith/YMJZEK5GQP5TWS4ZDLKGNC5OKH","download_json":"https://pith.science/pith/YMJZEK5GQP5TWS4ZDLKGNC5OKH.json","view_paper":"https://pith.science/paper/YMJZEK5G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.05036&json=true","fetch_graph":"https://pith.science/api/pith-number/YMJZEK5GQP5TWS4ZDLKGNC5OKH/graph.json","fetch_events":"https://pith.science/api/pith-number/YMJZEK5GQP5TWS4ZDLKGNC5OKH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YMJZEK5GQP5TWS4ZDLKGNC5OKH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YMJZEK5GQP5TWS4ZDLKGNC5OKH/action/storage_attestation","attest_author":"https://pith.science/pith/YMJZEK5GQP5TWS4ZDLKGNC5OKH/action/author_attestation","sign_citation":"https://pith.science/pith/YMJZEK5GQP5TWS4ZDLKGNC5OKH/action/citation_signature","submit_replication":"https://pith.science/pith/YMJZEK5GQP5TWS4ZDLKGNC5OKH/action/replication_record"}},"created_at":"2026-07-05T08:57:38.856658+00:00","updated_at":"2026-07-05T08:57:38.856658+00:00"}